Auto-Detection Method Using Convolution Neural Network for Bottom-Simulating Reflectors

In studies on gas hydrate,bottom-simulating reflectors(BSR)are used to determine the potential hydrate-bearing sedimentary layers.Usually,BSR detection is performed manually by experienced interpreters.Therefore,a method for implementing an auto-matic BSR detection process should be established.In this study,we develop a novel architecture for BSR characterization using the convolutional neural network(CNN)technique.We propose the use of Stokes’transform(ST)to obtain a time-frequency spectrum for the input of CNN.ST fully uses the frequency content of the seismic data,and a part of the 3D seismic data collected from the Blake Ridge is utilized to train the CNN.Synthetic seismic records with variable signal-to-noise ratios(SNR),as well as Blake Ridge seismic data,were used to validate the detection effect of the CNN.Results show that the CNN trained by this method exhibits excellent performance in noise-resistant testing and achieves an accuracy of more than 89% in field seismic data detection.

Analyses and Suggestions on China＇s Foreign Trade Development Pattern Transformation

In coping with the global financial crisis, all levels of the Chinese government and foreign trade firms have not only created new practices but have also changed their goal from transforming the growth pattern of foreign trade to transforming the development pattern of joreign trade. China＇s experience shows that economic instruments such as Net Barter Terms of Trade （NBTT） and the smile curve theory have limitations when it comes to interpreting foreign trade. If used improperly, scientific theories often lead to.fallacies. Transjbrming the development pattern of China＇s .foreign trade requires the following changes： national income distribution, foreign trade competition, market exploration, and resource utilization. We advise that competent authorities create a reasonable and operational system of assessment indicators.

A Novel Radial Basis Function Neural Network Approach for ECG Signal Classification

ions in the ECG signal.The cardiologist and medical specialistfind numerous difficulties in the process of traditional approaches.The specified restrictions are eliminated in the proposed classifier.The fundamental aim of this work is tofind the R-R interval.To analyze the blockage,different approaches are implemented,which make the computation as facile with high accuracy.The information are recovered from the MIT-BIH dataset.The retrieved data contain normal and pathological ECG signals.To obtain a noiseless signal,Gaborfilter is employed and to compute the amplitude of the signal,DCT-DOST(Discrete cosine based Discrete orthogonal stock well transform)is implemented.The amplitude is computed to detect the cardiac abnormality.The R peak of the underlying ECG signal is noted and the segment length of the ECG cycle is identified.The Genetic algorithm(GA)retrieves the primary highlights and the classifier integrates the data with the chosen attributes to optimize the identification.In addition,the GA helps in performing hereditary calculations to reduce the problem of multi-target enhancement.Finally,the RBFNN(Radial basis function neural network)is applied,which diminishes the local minima present in the signal.It shows enhancement in characterizing the ordinary and anomalous ECG signals.